针对无模铸造成形技术在铸型制作中的优势，基于聚晶金刚石（Polycrystalline diamond，PCD）刀具铣削砂型试验，研究了PCD刀具铣削砂型的磨损机理，构建了刀具磨损率的预测模型。使用扫描电镜观察了PCD刀具刀面磨损带的微观形貌，研究了刀具磨损随加工时间的变化规律和磨损机理。结果表明：在砂型加工过程中，凸起的砂粒对刀具表面的高频刻划和冲击导致刀具表面金刚石颗粒解理断裂和金刚石颗粒间黏结材料被刮除，从而造成金刚石颗粒脱落；刀具中的微观缺陷和砂型中的"硬点"造成刀具微崩刃。基于支持向量机（Support vector machines，SVM）回归分析算法，应用Matlab软件编译了PCD刀具相对加工量的磨损率回归程序，以正交试验结果为训练样本，构建了刀具磨损率预测模型。应用该模型预测了工艺参数对刀具磨损率的影响，为砂型高效加工的刀具磨损控制提供了依据。
For the advantage of patternless casting technology in mould making, the wear mechanism of PCD tool is studied and the prediction models of wear ratio are built based on the sand mould milling experiments with polycrystalline diamond (PCD) tool. The micro phase morphology of wear zone of PCD tool is observed by scanning electron microscopy, from which the change rule with the processing time and the wear mechanism are studied. The results show that the high frequency scrape and impact of the raised sand particles on the flank during the sand milling process lead to the cleavage fracture of diamond particles and the scraping of the bonding material among the PCD particles in the surface of the tool, which cause the diamond particles to fall off. Furthermore, the microscopic defects in the tool and the "hard spots" in the sand mould cause the cutter micro chipping. Based on support vector machine (SVM) regression analysis algorithm, Matlab is used to compile the regression program of the ratio of wear to milling volume to build the prediction model of wear ratio based on the orthogonal experimental results. The influence of process parameters on tool wear ratio are predicted with the model, which can provide the basis for the wear control of the tool with high efficiency processing sand mould.
[1] 单忠德. 无模铸造[M]. 北京:机械工业出版社,2017. SHAN Zhongde. Patternless casting[M]. Beijing:China Machine Press,2017.
[2] SHAN Zhongde,QIN Shaoyan,LIU Qian,et al. Key manufacturing technology & equipment for energy saving and emissions reduction in mechanical equipment industry[J]. International Journal of Precision Engineering and Manufacturing,2012,13(7):1095-1100.
[3] DONG Xiaoli,LI Xinya,SHAN Zhongde,et al. Rapid manufacturing of sand molds by direct milling[J]. Tsinghua Science and Technology,2009,14(6):212-215.
[4] 白清顺,姚英学,PHILLIP B,等. 聚晶金刚石刀具加工强化复合地板时的切削性能及磨损机理研究[J]. 摩擦学报,2003,23(2):81-85. BAI Qingshun,YAO Yingxue,PHILLIP B,et al. Wear mechanism and cutting performance of polycrystalline diamond tool in machining high pressure laminated flooring[J]. Tribology,2003,23(2):81-85.
[5] MOSELEY S G,BOHN K P,GOEDICKEMEIER M. Core drilling in reinforced concrete using polycrystalline diamond (PCD) cutters:Wear and fracture mechanisms[J]. International Journal of Refractory Metals & Hard Materials,2009,27(2):394-402.
[6] 葛英飞,徐九华,傅玉灿. 高速铣削SiCp/Al复合材料时聚晶金刚石刀具的磨损机理[J]. 光学精密工程,2011,19(12):2907-2918 GE Yingfei,XU Jiuhua,FU Yucan. Wear mechanisms of PCD of SiCp/Al tool in high speed milling composites[J]. Optics and Precision Engineering,2011,19(12):2907-2918.
[7] YAN Gang,YUE Wen,MENG Dezhong,et al. Wear performances and mechanisms of ultrahard polycrystalline diamond composite material grinded against granite[J]. International Journal of Refractory Metals & Hard Materials,2016,54:46-53.
[8] WEI Xinliang,QI Lehua,ZHOU Jiming,et al. Tool wear orphologies and mechanisms for cutting Cf/Mg composites[J]. International Journal of Advanced Manufacturing Technology, 2016,86(1-4):613-619.
[9] 秦国华,谢文斌,王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程,2015,23(5):1314-1320. QIN Guohua,XIE Wenbin,WANG Huamin. Detection and control for tool wear based on neural network and genetic algorithm[J]. Optics and Precision Engineering,2015,23(5):1314-1320.
[10] SHI Dongfeng,GINDY N N. Tool wear predictive model based on least squares support vector machines[J]. Mechanical Systems & Signal Processing,2007,21(4):1799-1814.
[11] TAN Yefa,HE Long,WANG Xiaolong,et al. Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings[J]. Transactions of Nonferrous Metals Society of China, 2014,24(8):2566-2573.
[12] ZHANG Haiyan,ZHANG Chen,ZHANG Jilin,et al. Tool wear model based on least squares support vector machines and Kalman filter[J]. Production Engineering,2014,8(1-2):101-109.
[13] GARCÍA-NIETO P J,GARCÍA-GONZALO E,VILÁN A J,et al. A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data[J]. International Journal of Advanced Manufacturing Technology,2016,86(1-4):769-780.
[14] 刘路,王太勇,蒋永翔,等. 基于超球面支持向量机的刀具磨损状态识别[J]. 农业机械学报,2011,42(1):218-222. LIU Lu,WANG Taiyong,JIANG Yongxiang,et al. Tool wear state recognition based on hyper-sphere support vector machine[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(1):218-222.
[15] 方开泰,刘民千,周永道. 试验设计与建模[M]. 北京:高等教育出版社,2011. FANG Kaitai,LIU Minqian,ZHOU Yongdao. Design and modeling of experiments[M]. Beijing:Higher Education Press,2011.
